A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent
Aizatul Shafiqah Mohd Faizal1, T Malathi Thevarajah2, Sook Mei Khor3
1Bioinformatics Programme, Institute of Biological Science, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia.
Insights
Artificial intelligence (AI) offers advanced methods for cardiovascular disease (CVD) risk prediction, complementing traditional statistical models. This review compares AI and conventional approaches, exploring biomarkers and future big data integration for improved CVD assessment.
Area of Science:
- Cardiology
- Medical Informatics
- Biomarker Discovery
Background:
- Cardiovascular disease (CVD) remains the leading global cause of mortality.
- Traditional statistical models are widely used for CVD risk prediction.
- The integration of artificial intelligence (AI) is transforming CVD risk assessment.
Purpose of the Study:
- To review and compare conventional risk scores and AI approaches for CVD risk prediction.
- To discuss the strengths and limitations of both traditional and AI methodologies.
- To explore biomarker discovery and future prospects in CVD risk assessment.
Main Methods:
- Comparative analysis of conventional statistical models and AI techniques in CVD risk prediction.
- Review of current literature on biomarker discovery for CVD.
- Exploration of challenges and future directions in CVD risk assessment.
Main Results:
- AI approaches demonstrate significant potential in enhancing CVD risk prediction accuracy and patient evaluation.
- Biomarkers play a crucial role in risk stratification and early CVD detection.
- Conventional methods have limitations that AI can potentially overcome.
Conclusions:
- AI represents a powerful advancement in cardiovascular disease risk prediction, offering advantages over traditional methods.
- Biomarker discovery is essential for early detection and improved risk stratification.
- Future CVD risk assessment will likely involve multi-modal big data integration and advanced AI techniques.
Abstract:
Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global health issue. Traditionally, statistical models are used commonly in the risk prediction and assessment of CVD. However, the adoption of artificial intelligent (AI) approach is rapidly taking hold in the current era of technology to evaluate patient risks and predict the outcome of CVD. In this review, we outline various conventional risk scores and prediction models and do a comparison with the AI approach. The strengths and limitations of both conventional and AI approaches are discussed. Besides that, biomarker discovery related to CVD are also elucidated as the biomarkers can be used in the risk stratification as well as early detection of the disease. Moreover, problems and challenges involved in current CVD studies are explored. Lastly, future prospects of CVD risk prediction and assessment in the multi-modality of big data integrative approaches are proposed.
